How Large Language Models Are Trained (in Plain English)

Chat assistants seem magical until you see the recipe. Training happens in several stages.

The model reads a huge collection of text (books, web pages, code, articles) and learns to predict the next token. Doing this billions of times teaches grammar, facts, styles and patterns of reasoning.

• It requires enormous computing power (thousands of specialised chips running for weeks or months).
• The result is a base model that continues text but isn't yet a helpful assistant.

The base model is trained on examples of good conversations, such as questions and helpful answers, so it learns to follow instructions and respond in a useful format.

Humans (and sometimes other AI models) compare or rate responses. The model is adjusted to produce answers that people prefer: more helpful, honest and safe. This is often called reinforcement learning from feedback.

• Red-teaming: experts try to make the model misbehave.
• Evaluations: benchmarks and real-task tests.
• Safeguards: refusals for harmful requests, content filters and monitoring.

The model is served through apps and APIs. Providers release improved versions, and may add tools such as search, code execution and retrieval.

• It learns statistical patterns, not a database of facts, which is why it can hallucinate.
• Training data shapes behaviour, including biases.
• Knowledge has a cutoff date, unless tools provide fresh information.
• It doesn't learn from your chat in real time in the way people do.
• Training choices (data, feedback, safety) strongly affect the model's personality and limits.

Copyright of training data, energy use, how to evaluate truthfulness, and how to make models more transparent.

LLMs are pattern-learning machines refined by human feedback. That explains both their strengths and their quirks.